Autonomic nervous system and hypothalamic–pituitary–adrenal axis response to experimentally induced cold pain in adolescent non-suicidal self-injury – study protocol

Background: Adolescent non-suicidal self-injury (NSSI) is associated with altered sensitivity to experimentally induced pain. Adolescents engaging in NSSI report greater pain threshold and pain tolerance, as well as lower pain intensity and pain unpleasantness compared to healthy controls. The experience of pain is associated with reactivity of both the autonomic nervous system (ANS) and the hypothalamic–pituitary–adrenal (HPA) axis. However, previous research has not yet systematically addressed differences in the physiological response to experimentally induced pain comparing adolescents with NSSI and age- and sex-matched healthy controls. Methods/Design: Adolescents with NSSI and healthy controls undergo repeated painful stimulation with the cold pressor task. ANS activity is continuously recorded throughout the procedure to assess changes in heart rate and heart rate variability. Blood pressure is monitored and saliva is collected prior to and after nociceptive stimulation to assess levels of saliva cortisol. Discussion: The study will provide evidence whether lower pain sensitivity in adolescents with NSSI is associated with blunted physiological and endocrinological responses to experimentally induced pain compared to healthy controls. Extending on the existing evidence on altered pain sensitivity in NSSI, measured by self-reports and behavioural assessments, this is the first study to take a systematic approach in evaluating the physiological response to experimentally induced pain in adolescent NSSI. Trial Registration: Deutsche Register Klinischer Studien, Study ID: DRKS00007807 ; Trial Registration Date: 13.02.201

Effects of vibroacoustic stimulation in music therapy for palliative care patients: a feasibility study

Background: The present study aimed at examining whether methodological strategies from a previously implemented study design could be transferred to the evaluation of the psychological and physiological effects of a music therapy intervention working with vibroacoustic stimulation in palliative care. Method: Nine participants suffering from advanced cancer took part in single-sessions of music therapy, lasting for 30 min. The live music therapy intervention utilized singing chair sounds and vocal improvisation. Visual analogue scales (VAS) were used to assess self-ratings of pain, relaxation, and well-being before and after each session. During the intervention, we continuously recorded heart rate variability (HRV) as a measure of autonomic functioning. Data collection was complemented by a semi-structured interview to explore subjective experiences in more detail. Feasibility was defined as the ability to complete 80 % of the sessions in accordance with the study protocol. Results: In 5 out of 9 sessions (55 %) it was possible to deliver the intervention and obtain all data as intended. VAS assessment was feasible, although graphical and statistical examination revealed only marginal mean changes between pre and post. HRV recordings were subject to artifacts. While HRV parameters differed between individuals, mean changes over time remained relatively constant. Interview data confirmed that the individual perception was very heterogeneous, ranging from “calming” to “overwhelming”. Conclusion: The criterion of feasibility was not met in this study. Physiological data showed high attrition rates, most likely due to movement artifacts and reduced peripheral blood flow in some participants’ extremities. Examination of individual-level trajectories revealed that vibroacoustic stimulation may have an impact on the autonomic response. However, the direction and mechanisms of effects needs to be further explored in future studies. Trial registration: German Clinical Trials Register – DRKS00006137 (July 4th, 2014)

"Song of Life (SOL)" study protocol: a multicenter, randomized trial on the emotional, spiritual, and psychobiological effects of music therapy in palliative care

Background: Although patients in palliative care commonly report high emotional and spiritual needs, effective psychosocial treatments based on high quality studies are rare. First research provides evidence for benefits of psychosocial interventions in advanced cancer care. To specifically address end-of-life care requirements, life review techniques and creative-arts based therapies offer a promising potential. Therefore, the present study protocol presents a randomized controlled trial on the effectiveness of a newly developed music therapy technique that is based on a biographically meaningful song (“Song of Life”; SOL). Methods: In a design with two parallel arms, 104 patients at two palliative care units will be randomly assigned to three sessions of either SOL (experimental group) or relaxation exercises (control group). Improvements in the psychological domain of quality of life will be the primary endpoint, while secondary outcomes encompass spiritual well-being, ego-integrity, overall quality of life, and distress. Additionally, caregivers will be asked to provide feedback about the treatment. Assessment of biopsychological stress markers and qualitative analysis of perceived strengths and weaknesses will complement data collection. Discussion: Based on the results of a previous pilot study, we dedicated considerable efforts to optimizing the intervention and selecting appropriate outcomes for the present trial. We are confident to have designed a methodologically rigorous study that will contribute to the evidence-base and help to develop the potential of psychosocial interventions in palliative care. Trial registration: German Clinical Trials Register (DRKS) – DRKS00015308 (date of registration: September 07th 2018)

Comparative investigation of mathematical methods for modeling and optimization of Common-Rail DI diesel engines

In the following work, a phenomenological/knowledge based model and a “blackbox” approach for the simulation and optimization of Common-Rail DI diesel engines are developed and comparatively evaluated. The evaluation, which is carried out for a comprehensive sample of engines and operating conditions, focuses on the ability of both approaches to yield predictive measures of the in-cylinder combustion process, as well as the engine out exhaust emissions. The phenomenological/knowledge based model expands an existing, simple, yet physically and chemically accurate model by implementing Evolutionary Algorithms to calibrate the model parameters. As is shown through comprehensive investigations using measurements from an automotive, a heavy-duty, and a two-stroke marine diesel engine, the new models are able to determine the qualitative and quantitative Rates Of Heat Release (ROHR), nitrogen oxide and soot emissions across an entire engine operating map within a matter of seconds. To evaluate the general applicability of the model, a version of the model calibrated to one engine (for example the heavy-duty engine) is directly applied to another engine (for example the marine diesel engine), without recalibrating the model parameters. For such a “blind try” investigation, it is seen that because the phenomenological model considers the appropriate physical and chemical processes, it is capable of providing extrapolative predictions. In addition to evaluating the model based on a comparison of calculations and measurements from applied combustion systems, a detailed investigation of the model itself is carried out. In particular, a sensitivity analysis of the model specific parameters and statistical analyses are used to evaluate the modeling and optimization performance of the model. From such an analysis of the ROHR model, it is shown, among other things, that: (i) the accuracy of the model depends on the calibration algorithm, (ii) there are only negligible differences due to stochastic parameter initialization when using Evolutionary Algorithms, and (iii) the chemical and physical effects seen during the implementation of alternative fuels, such as diesel-water emulsions and diesel-butylal blends are correctly represented by the ROHR submodel. Furthermore, from the detailed analysis of the emission models, a larger sensitivity of the model to small parameter changes is seen, as is a general influence of the operating conditions on the model accuracy. Based on a comparison of engine variables, such as the cylinder pressure and temperature, nitrogen oxide and soot emissions, determined from measurement and simulation results, the ability of the phenomenological model to predict the combustion and emission formation processes is unambiguously verified. Although a wide range of engine operating conditions are considered in this comparison, only small deviations (less than 10 %) are seen between the measured and calculated engine variables, with the exception of the maximum rate of pressure rise. As an alternative to the phenomenological/knowledge based model approach, an Artificial Neural Network (ANN) is also investigated as a representative “black-box” approach. From a comparison of these two approaches, based on their abilities to predict ROHR parameters, nitrogen oxide and soot emissions, it is seen, that the ANN is more easily adapted to different engine configurations and provides better agreement with the measured calibration (i.e. training) data. However, when the models are used to predict the ROHR characteristics and exhaust emissions for operating conditions to which they were not trained, the ANN is not able to match the extrapolative ability of the phenomenological/knowledge based model, which provides better agreement with the measured values. As is shown through the comparison of the two approaches, the phenomenological/knowledge based model and ANN have different strengths and weaknesses, and depending on the intended application, one approach will have distinct advantages over the other. The decision as to which approach is better suited will be based, in part, on the available experimental data, the overall knowledge of the system being considered, the time available for the investigation (both for the actual calculations and the development of the approach), as well as the necessity for extrapolative calculations. The phenomenological/knowledge based model approach is preferred when qualitative predictions based on fundamental knowledge are essential, while the ANN is preferred when the fast analysis of comprehensive experimental measurements, without fundamental knowledge of the physical and chemical processes, is required. Overall, the more general applicability, the more consistent qualitative results, and the possibility for extrapolative investigations make the phenomenological/knowledge based approach the more appropriate choice for the majority of applications, particularly for future engine developments. Gegenstand der vorliegenden Arbeit ist die Herleitung und vergleichende Untersuchung eines modell/wissensbasierten und eines “black-box” Ansatzes zur innermotorischen Simulation und Optimierung der Verbrennung sowie Schadstoffentstehung in direkt eingespritzten Common-Rail Diesel Motoren. Die hierzu entwikkelten Ansätze und Modelle werden für eine umfangreiche Palette von unterschiedlichen Motoren und Betriebszustände angewandt. Der neu entwickelte, modell-/wissensbasierte Ansatz baut auf einfachen, jedoch physikalisch und chemisch korrekten phänomenologischen Modellen auf, welche mittels evolutionärer Algorithmen kalibriert werden. Wie in umfangreichen Untersuchungen an einem Automobil-, einem Nutzfahrzeug-, und einem Schiffsantrieb erfolgreich gezeigt werden konnte, erlaubt der Ansatz die kennfeldweite, qualitative und quantitative Berechnung von Brennverläufen, Stickoxid- und Russemissionen innerhalb weniger Sekunden. Anhand von sogenannten “blinden Versuchen”, in welchen kalibrierte Modelle eines Motors ohne Anpassung der Parameter auf einen anderen Motor übertragen wurden (z.B. das für den Nutzfahrzeugmotor kalibrierte Brennverlaufsmodell wird zur Berechnung des Schiffsantriebs verwendet), konnte des weiteren gezeigt werden, dass die Verwendung geeigneter physikalisch/chemisch basierter Modelle selbst extrapolative Abschätzungen ermöglicht. Neben den stark anwendungsorientierten Vergleichen von experimentellen und berechneten Kenngrössen für die jeweiligen Betriebspunkte wurden für alle Modelle auch detaillierte Untersuchungen (z.B. Parameter Sensitivitätsstudien) und statistische Analysen zu speziellen Modellierungs- und Optimierungsaspekten durchgeführt. Die detaillierte Analyse der Brennverlaufsmodellierung ergab dabei unter anderem eine differenzierte Abhängigkeit der Modellqualität von verschiedenen Kalibrierungsalgorithmen, vernachlässigbare Abweichungen aufgrund stochastischen Parameterinitialisierung bei evolutionären Algorithmen, sowie die korrekte Abbildung der physikalischen und chemischen Einflüsse unterschiedlicher Kraftstoffe wie Diesel-Wasser-Emulsionen oder Diesel-Butylal-Gemische. Am Beispiel der Schadstoffmodellierungen konnten ferner stark unterschiedliche Sensitivitäten der Modelle sowohl bei geringen Parameteränderungen, als auch zwischen verschiedenen Betriebspunkten im Allgemeinen, gezeigt werden. Mittels eines Vergleichs von experimentell und numerisch ermittelten Motorprozessgrössen, wie zum Beispiel Zylinderdruck und -temperatur, Stickoxid und Russ Emissionen, wird das Potential der phänomenologischen Modelle zur Vorausberechnung motorischer Vorgänge anschaulich aufgezeigt. Über alle Betriebspunkte gesehen weisen dabei die betrachteten Kenngrössen, mit Ausnahme der maximalen Druckanstiege, lediglich Fehler im tiefen einstelligen Prozentbereich auf. Als Gegenstück zur Untersuchung des modell-/wissensbasierten Ansatzes werden in dieser Arbeit künstliche Neuronale Netze (engl.: Artificial Neural Networks ANN), als “Schulbeispiel” für black-box Ansätze, verwendet. Am Beispiel der Modellierung und Simulation, bzw. Training und Verifikation, der Brennverlaufscharakteristika, Stickoxid- und Russemissionen konnten sowohl eine exzellente Adaptierbarkeit der Netze für alle Motor und Modell Kombinationen, wie auch eine reduzierte Extrapolierbarkeit der trainierten Netze nachgewiesen werden. Während die Abweichungen zwischen den experimentellen und simulierten Ergebnissen für trainierte Betriebspunkte deutlich geringer ausfielen als bei den phänomenologischen Modellen, verhielt es sich bei der Verifikation, bzw. Extrapolation der Betriebpunkte gerade umgekehrt, d.h. es kommt zu einer signifikanten Verminderung der Qualität der simulierten Ergebnisse bei den künstliche Neuronalen Netzen. Wie durch den Vergleich der beiden Ansätze gezeigt werden kann, verfügen sowohl der modell-/wissensbasierte als auch der black-box Ansatz über Stärken und Schwächen, welche abhängig vom Fokus der Untersuchung, den Ausschlag für den einen beziehungsweise anderen Ansatz geben. Der Entscheid welcher Ansatz letztendlich besser geeignet ist, ist dabei unteranderem abhängig von den verfügbaren experimentellen Daten, den Kenntnissen vom betrachteten System, den zeitlichen Rahmenbedingungen (sowohl für die Entwicklung des Ansatzes, als auch die eigentlichen Berechnungen), und der Notwendigkeit von extrapolativen Berechnungen. Der modell/wissensbasierten Ansatz eignet sich für qualitativ zuverlässige Vorhersagen basierend auf fundiertem Wissen, während der black-box Ansatz für schnelle Analysen von umfangreichen experimentellen Daten ohne fundierte Kenntnisse zu den physikalisch/chemischen Zusammenhängen anerbietet. Die breitere Anwendbarkeit, sowie die qualitativ konstanteren Resultate und die Möglichkeit der Extrapolation der Berechnungen lassen für die meisten Anwendungen eine Präferenz hin zu wissensbasierten Modellen erkennen

Instructed partnership appreciation in depression: Effects on mood, momentary relationship satisfaction, and stress reactivity [dataset]

This study compared the affective and psychobiological response of depressed and non-depressed romantic couples in an instructed partnership appreciation task (PAT) scenario that included positive feedback and appreciative communication. With the observation of (close to) naturalistic behavior between real-life partners and the emphasis on positive interaction, we aimed at extending previous research that rather focused on conflict behavior or non-intimate laboratory stressors. The general hypothesis was that couples with depression, and the depressed female index-patients in particular, would benefit less from instructed positive couple interaction, in comparison to healthy controls. A total of 184 individuals were included. Contrary to our expectation, depressed couples’ subjective mood and momentary relationship satisfaction improved in response to the PAT. At the same time, cortisol outputs were higher in depressed women than in healthy controls, particularly if participants were in a longer-term relationship. This data suggests, that instructed and individualized positive feedback between partners can have a psychobiological effect on women with depression, with potential implications for treatment